Evaluating Model Performance Through a User-Centric Explainable Framework for Probabilistic Load Forecasting Models

Rebecca Robin, Leena Heistrene, Juri Belikov, Dmitry Baimel, Yoash Levron

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Load forecasting models ensure efficient, secure, and stable operation of the modern power system. Probabilistic forecasting accounts for uncertainties associated with missing features that are often overlooked by deterministic approaches. However, machine learning-based probabilistic models are complicated. This paper proposes a user-centric explainable AI framework that presents global and local interpretations aligned with the expertise and explanation needs of the targeted user. The overall influence of temporal and spatial exogenous features at the model development stage is evaluated using the Permutation Feature Importance technique. Such an explanation provides a holistic picture of the knowledge gained by the Gradient Boosting Regressor-based probabilistic load forecasting model. Further-more, the proposed framework suggests the implementation of SHapely Additive exPlanations (SHAP) at the post-deployment stage for individual forecast instances. Local explanations provided by SHAP are used to distinguish between interval forecasts with higher and lower forecast accuracy. Such distinction is applied for both the lower and upper bounds of the forecast interval. This is specifically useful for the non-AI expert end-users that need load forecasts for their strategizing their daily operations. This work is validated on the Kaggle data set on the national load demand of Panama supported with several other exogenous features such as weather-related quantities, holidays, and date-time details. Results show the efficacy of the proposed framework and its ability to provide user-friendly interpretations aligned with users' explanation goals.

Original languageEnglish
Title of host publication2024 3rd International Conference on Power, Control and Computing Technologies, ICPC2T 2024
Pages427-432
Number of pages6
ISBN (Electronic)9798350349207
DOIs
StatePublished - 2024
Event3rd International Conference on Power, Control and Computing Technologies, ICPC2T 2024 - Raipur, India
Duration: 18 Jan 202420 Jan 2024

Publication series

Name2024 3rd International Conference on Power, Control and Computing Technologies, ICPC2T 2024

Conference

Conference3rd International Conference on Power, Control and Computing Technologies, ICPC2T 2024
Country/TerritoryIndia
CityRaipur
Period18/01/2420/01/24

Keywords

  • Explainable AI
  • Gradient Boost Regressor
  • load forecasting
  • PFI
  • power system
  • SHAP
  • XAI

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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